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Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

Chavalarias D, Cointet JP - PLoS ONE (2013)

Bottom Line: We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields.We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns.Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

View Article: PubMed Central - PubMed

Affiliation: Complex Systems Institute of Paris Ile-de-France, Paris, France. david.chavalarias@ehess.fr

ABSTRACT
We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

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Variation of the rate of emergence (A) and conceptual recombinations (B) in the phylomemy, in the vicinity of a branching node.We observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. Error bars represent the 95% confidence interval.
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pone-0054847-g009: Variation of the rate of emergence (A) and conceptual recombinations (B) in the phylomemy, in the vicinity of a branching node.We observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. Error bars represent the 95% confidence interval.

Mentions: The normalized rates of conceptual emergence and recombination in the phylomemy, in the vicinity of branching and merging events, are a bit noisy; nevertheless, they clearly differ suggesting differences in the terms-level dynamics for these two kind of events (cf.Fig. 9 and Fig. 10). At the branching nodes of the phylomemy, we observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. On the contrary, for merging events, we observe a peak of conceptual emergence one period before merging, and a rate of conceptual emergence below average at merging nodes; while the rate of recombination is above average at merging nodes and is a monotonous increasing function in the vicinity of merging nodes.


Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

Chavalarias D, Cointet JP - PLoS ONE (2013)

Variation of the rate of emergence (A) and conceptual recombinations (B) in the phylomemy, in the vicinity of a branching node.We observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. Error bars represent the 95% confidence interval.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3569444&req=5

pone-0054847-g009: Variation of the rate of emergence (A) and conceptual recombinations (B) in the phylomemy, in the vicinity of a branching node.We observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. Error bars represent the 95% confidence interval.
Mentions: The normalized rates of conceptual emergence and recombination in the phylomemy, in the vicinity of branching and merging events, are a bit noisy; nevertheless, they clearly differ suggesting differences in the terms-level dynamics for these two kind of events (cf.Fig. 9 and Fig. 10). At the branching nodes of the phylomemy, we observe a rate of conceptual emergence which is above average and quickly drops at the following period; whereas the proportion of recombinations is below average and quickly increases at the following period. On the contrary, for merging events, we observe a peak of conceptual emergence one period before merging, and a rate of conceptual emergence below average at merging nodes; while the rate of recombination is above average at merging nodes and is a monotonous increasing function in the vicinity of merging nodes.

Bottom Line: We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields.We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns.Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

View Article: PubMed Central - PubMed

Affiliation: Complex Systems Institute of Paris Ile-de-France, Paris, France. david.chavalarias@ehess.fr

ABSTRACT
We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

Show MeSH
Related in: MedlinePlus